Overview

Dataset statistics

Number of variables28
Number of observations5735
Missing cells11659
Missing cells (%)7.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory224.0 B

Variable types

Numeric20
Categorical8

Alerts

ALQ130 is highly overall correlated with ALQ110High correlation
DMDMARTL is highly overall correlated with ALQ110High correlation
BPXSY1 is highly overall correlated with BPXSY2High correlation
BPXDI1 is highly overall correlated with BPXDI2High correlation
BPXSY2 is highly overall correlated with BPXSY1High correlation
BPXDI2 is highly overall correlated with BPXDI1High correlation
BMXWT is highly overall correlated with BMXBMI and 3 other fieldsHigh correlation
BMXHT is highly overall correlated with BMXLEG and 2 other fieldsHigh correlation
BMXBMI is highly overall correlated with BMXWT and 2 other fieldsHigh correlation
BMXLEG is highly overall correlated with BMXHT and 2 other fieldsHigh correlation
BMXARML is highly overall correlated with BMXWT and 3 other fieldsHigh correlation
BMXARMC is highly overall correlated with BMXWT and 2 other fieldsHigh correlation
BMXWAIST is highly overall correlated with BMXWT and 2 other fieldsHigh correlation
ALQ110 is highly overall correlated with ALQ130 and 2 other fieldsHigh correlation
SMQ020 is highly overall correlated with ALQ110High correlation
RIAGENDR is highly overall correlated with BMXHT and 2 other fieldsHigh correlation
SMQ020 is highly imbalanced (50.4%)Imbalance
DMDCITZN is highly imbalanced (65.9%)Imbalance
HIQ210 is highly imbalanced (70.0%)Imbalance
ALQ101 has 527 (9.2%) missing valuesMissing
ALQ110 has 4004 (69.8%) missing valuesMissing
ALQ130 has 2356 (41.1%) missing valuesMissing
DMDEDUC2 has 261 (4.6%) missing valuesMissing
DMDMARTL has 261 (4.6%) missing valuesMissing
INDFMPIR has 601 (10.5%) missing valuesMissing
BPXSY1 has 334 (5.8%) missing valuesMissing
BPXDI1 has 334 (5.8%) missing valuesMissing
BPXSY2 has 200 (3.5%) missing valuesMissing
BPXDI2 has 200 (3.5%) missing valuesMissing
BMXWT has 69 (1.2%) missing valuesMissing
BMXHT has 62 (1.1%) missing valuesMissing
BMXBMI has 73 (1.3%) missing valuesMissing
BMXLEG has 390 (6.8%) missing valuesMissing
BMXARML has 308 (5.4%) missing valuesMissing
BMXARMC has 308 (5.4%) missing valuesMissing
BMXWAIST has 367 (6.4%) missing valuesMissing
HIQ210 has 1003 (17.5%) missing valuesMissing
ALQ130 is highly skewed (γ1 = 28.82541968)Skewed
SEQN has unique valuesUnique
INDFMPIR has 59 (1.0%) zerosZeros

Reproduction

Analysis started2023-06-05 16:46:50.513464
Analysis finished2023-06-05 16:49:58.132093
Duration3 minutes and 7.62 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

SEQN
Real number (ℝ)

Distinct5735
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88678.583
Minimum83732
Maximum93702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.9 KiB
2023-06-05T21:49:58.696573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum83732
5-th percentile84225.7
Q186164
median88668
Q391178.5
95-th percentile93191.3
Maximum93702
Range9970
Interquartile range (IQR)5014.5

Descriptive statistics

Standard deviation2882.1392
Coefficient of variation (CV)0.032500962
Kurtosis-1.2088354
Mean88678.583
Median Absolute Deviation (MAD)2508
Skewness0.020082912
Sum5.0857168 × 108
Variance8306726.6
MonotonicityStrictly increasing
2023-06-05T21:49:59.150310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83732 1
 
< 0.1%
90319 1
 
< 0.1%
90347 1
 
< 0.1%
90346 1
 
< 0.1%
90344 1
 
< 0.1%
90341 1
 
< 0.1%
90340 1
 
< 0.1%
90338 1
 
< 0.1%
90337 1
 
< 0.1%
90335 1
 
< 0.1%
Other values (5725) 5725
99.8%
ValueCountFrequency (%)
83732 1
< 0.1%
83733 1
< 0.1%
83734 1
< 0.1%
83735 1
< 0.1%
83736 1
< 0.1%
83737 1
< 0.1%
83741 1
< 0.1%
83742 1
< 0.1%
83743 1
< 0.1%
83744 1
< 0.1%
ValueCountFrequency (%)
93702 1
< 0.1%
93700 1
< 0.1%
93697 1
< 0.1%
93696 1
< 0.1%
93695 1
< 0.1%
93691 1
< 0.1%
93690 1
< 0.1%
93689 1
< 0.1%
93685 1
< 0.1%
93684 1
< 0.1%

ALQ101
Categorical

Distinct3
Distinct (%)0.1%
Missing527
Missing (%)9.2%
Memory size44.9 KiB
1.0
3477 
2.0
1728 
9.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15624
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 3477
60.6%
2.0 1728
30.1%
9.0 3
 
0.1%
(Missing) 527
 
9.2%

Length

2023-06-05T21:49:59.490266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T21:49:59.968074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3477
66.8%
2.0 1728
33.2%
9.0 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 5208
33.3%
0 5208
33.3%
1 3477
22.3%
2 1728
 
11.1%
9 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10416
66.7%
Other Punctuation 5208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5208
50.0%
1 3477
33.4%
2 1728
 
16.6%
9 3
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 5208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 5208
33.3%
0 5208
33.3%
1 3477
22.3%
2 1728
 
11.1%
9 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 5208
33.3%
0 5208
33.3%
1 3477
22.3%
2 1728
 
11.1%
9 3
 
< 0.1%

ALQ110
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)0.2%
Missing4004
Missing (%)69.8%
Memory size44.9 KiB
2.0
979 
1.0
747 
9.0
 
4
7.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5193
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 979
 
17.1%
1.0 747
 
13.0%
9.0 4
 
0.1%
7.0 1
 
< 0.1%
(Missing) 4004
69.8%

Length

2023-06-05T21:50:00.266760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T21:50:00.616334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 979
56.6%
1.0 747
43.2%
9.0 4
 
0.2%
7.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 1731
33.3%
0 1731
33.3%
2 979
18.9%
1 747
14.4%
9 4
 
0.1%
7 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3462
66.7%
Other Punctuation 1731
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1731
50.0%
2 979
28.3%
1 747
21.6%
9 4
 
0.1%
7 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 1731
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5193
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1731
33.3%
0 1731
33.3%
2 979
18.9%
1 747
14.4%
9 4
 
0.1%
7 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1731
33.3%
0 1731
33.3%
2 979
18.9%
1 747
14.4%
9 4
 
0.1%
7 1
 
< 0.1%

ALQ130
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct15
Distinct (%)0.4%
Missing2356
Missing (%)41.1%
Infinite0
Infinite (%)0.0%
Mean3.9115123
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.9 KiB
2023-06-05T21:50:00.986443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum999
Range998
Interquartile range (IQR)2

Descriptive statistics

Standard deviation34.341839
Coefficient of variation (CV)8.7796833
Kurtosis833.2248
Mean3.9115123
Median Absolute Deviation (MAD)1
Skewness28.82542
Sum13217
Variance1179.3619
MonotonicityNot monotonic
2023-06-05T21:50:01.584534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 1164
20.3%
2 971
16.9%
3 482
 
8.4%
4 248
 
4.3%
6 174
 
3.0%
5 149
 
2.6%
8 47
 
0.8%
10 40
 
0.7%
12 39
 
0.7%
7 27
 
0.5%
Other values (5) 38
 
0.7%
(Missing) 2356
41.1%
ValueCountFrequency (%)
1 1164
20.3%
2 971
16.9%
3 482
8.4%
4 248
 
4.3%
5 149
 
2.6%
6 174
 
3.0%
7 27
 
0.5%
8 47
 
0.8%
9 7
 
0.1%
10 40
 
0.7%
ValueCountFrequency (%)
999 4
 
0.1%
15 20
 
0.3%
14 5
 
0.1%
12 39
 
0.7%
11 2
 
< 0.1%
10 40
 
0.7%
9 7
 
0.1%
8 47
 
0.8%
7 27
 
0.5%
6 174
3.0%

SMQ020
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.9 KiB
2
3406 
1
2319 
9
 
8
7
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5735
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 3406
59.4%
1 2319
40.4%
9 8
 
0.1%
7 2
 
< 0.1%

Length

2023-06-05T21:50:01.969250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T21:50:02.386166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 3406
59.4%
1 2319
40.4%
9 8
 
0.1%
7 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 3406
59.4%
1 2319
40.4%
9 8
 
0.1%
7 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5735
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3406
59.4%
1 2319
40.4%
9 8
 
0.1%
7 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5735
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3406
59.4%
1 2319
40.4%
9 8
 
0.1%
7 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3406
59.4%
1 2319
40.4%
9 8
 
0.1%
7 2
 
< 0.1%

RIAGENDR
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.9 KiB
2
2976 
1
2759 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5735
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 2976
51.9%
1 2759
48.1%

Length

2023-06-05T21:50:02.729906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T21:50:03.116606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 2976
51.9%
1 2759
48.1%

Most occurring characters

ValueCountFrequency (%)
2 2976
51.9%
1 2759
48.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5735
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2976
51.9%
1 2759
48.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5735
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2976
51.9%
1 2759
48.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2976
51.9%
1 2759
48.1%

RIDAGEYR
Real number (ℝ)

Distinct63
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.05231
Minimum18
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.9 KiB
2023-06-05T21:50:03.502357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q132
median48
Q363
95-th percentile80
Maximum80
Range62
Interquartile range (IQR)31

Descriptive statistics

Standard deviation18.431011
Coefficient of variation (CV)0.3835614
Kurtosis-1.1339044
Mean48.05231
Median Absolute Deviation (MAD)15
Skewness0.1036458
Sum275580
Variance339.70218
MonotonicityNot monotonic
2023-06-05T21:50:03.986410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 343
 
6.0%
18 133
 
2.3%
19 128
 
2.2%
60 119
 
2.1%
61 112
 
2.0%
51 109
 
1.9%
36 108
 
1.9%
26 108
 
1.9%
54 108
 
1.9%
31 105
 
1.8%
Other values (53) 4362
76.1%
ValueCountFrequency (%)
18 133
2.3%
19 128
2.2%
20 79
1.4%
21 59
1.0%
22 95
1.7%
23 100
1.7%
24 88
1.5%
25 104
1.8%
26 108
1.9%
27 101
1.8%
ValueCountFrequency (%)
80 343
6.0%
79 35
 
0.6%
78 47
 
0.8%
77 43
 
0.7%
76 44
 
0.8%
75 62
 
1.1%
74 52
 
0.9%
73 58
 
1.0%
72 62
 
1.1%
71 66
 
1.2%

RIDRETH1
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.9 KiB
3
1839 
4
1227 
1
1018 
5
901 
2
750 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5735
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3 1839
32.1%
4 1227
21.4%
1 1018
17.8%
5 901
15.7%
2 750
13.1%

Length

2023-06-05T21:50:04.509047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T21:50:04.936254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 1839
32.1%
4 1227
21.4%
1 1018
17.8%
5 901
15.7%
2 750
13.1%

Most occurring characters

ValueCountFrequency (%)
3 1839
32.1%
4 1227
21.4%
1 1018
17.8%
5 901
15.7%
2 750
13.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5735
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1839
32.1%
4 1227
21.4%
1 1018
17.8%
5 901
15.7%
2 750
13.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5735
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1839
32.1%
4 1227
21.4%
1 1018
17.8%
5 901
15.7%
2 750
13.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1839
32.1%
4 1227
21.4%
1 1018
17.8%
5 901
15.7%
2 750
13.1%

DMDCITZN
Categorical

Distinct4
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size44.9 KiB
1.0
4746 
2.0
975 
7.0
 
8
9.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17202
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 4746
82.8%
2.0 975
 
17.0%
7.0 8
 
0.1%
9.0 5
 
0.1%
(Missing) 1
 
< 0.1%

Length

2023-06-05T21:50:05.361479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T21:50:05.757321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4746
82.8%
2.0 975
 
17.0%
7.0 8
 
0.1%
9.0 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 5734
33.3%
0 5734
33.3%
1 4746
27.6%
2 975
 
5.7%
7 8
 
< 0.1%
9 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11468
66.7%
Other Punctuation 5734
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5734
50.0%
1 4746
41.4%
2 975
 
8.5%
7 8
 
0.1%
9 5
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 5734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17202
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 5734
33.3%
0 5734
33.3%
1 4746
27.6%
2 975
 
5.7%
7 8
 
< 0.1%
9 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 5734
33.3%
0 5734
33.3%
1 4746
27.6%
2 975
 
5.7%
7 8
 
< 0.1%
9 5
 
< 0.1%

DMDEDUC2
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing261
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean3.4417245
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.9 KiB
2023-06-05T21:50:06.088563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q34.75
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1.75

Descriptive statistics

Standard deviation1.3096997
Coefficient of variation (CV)0.3805359
Kurtosis-0.69020787
Mean3.4417245
Median Absolute Deviation (MAD)1
Skewness-0.45549422
Sum18840
Variance1.7153134
MonotonicityNot monotonic
2023-06-05T21:50:06.393063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 1621
28.3%
5 1366
23.8%
3 1186
20.7%
1 655
11.4%
2 643
 
11.2%
9 3
 
0.1%
(Missing) 261
 
4.6%
ValueCountFrequency (%)
1 655
11.4%
2 643
 
11.2%
3 1186
20.7%
4 1621
28.3%
5 1366
23.8%
9 3
 
0.1%
ValueCountFrequency (%)
9 3
 
0.1%
5 1366
23.8%
4 1621
28.3%
3 1186
20.7%
2 643
 
11.2%
1 655
11.4%

DMDMARTL
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.1%
Missing261
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean2.628608
Minimum1
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.9 KiB
2023-06-05T21:50:06.718167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile6
Maximum77
Range76
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3667855
Coefficient of variation (CV)0.90039502
Kurtosis354.37032
Mean2.628608
Median Absolute Deviation (MAD)0
Skewness11.645494
Sum14389
Variance5.6016737
MonotonicityNot monotonic
2023-06-05T21:50:07.018259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 2780
48.5%
5 1004
 
17.5%
3 579
 
10.1%
6 527
 
9.2%
2 396
 
6.9%
4 186
 
3.2%
77 2
 
< 0.1%
(Missing) 261
 
4.6%
ValueCountFrequency (%)
1 2780
48.5%
2 396
 
6.9%
3 579
 
10.1%
4 186
 
3.2%
5 1004
 
17.5%
6 527
 
9.2%
77 2
 
< 0.1%
ValueCountFrequency (%)
77 2
 
< 0.1%
6 527
 
9.2%
5 1004
 
17.5%
4 186
 
3.2%
3 579
 
10.1%
2 396
 
6.9%
1 2780
48.5%

DMDHHSIZ
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3238012
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.9 KiB
2023-06-05T21:50:07.419699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7246697
Coefficient of variation (CV)0.51888474
Kurtosis-0.64203028
Mean3.3238012
Median Absolute Deviation (MAD)1
Skewness0.53882754
Sum19062
Variance2.9744857
MonotonicityNot monotonic
2023-06-05T21:50:07.807159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 1546
27.0%
3 1037
18.1%
4 936
16.3%
1 770
13.4%
5 699
12.2%
6 379
 
6.6%
7 368
 
6.4%
ValueCountFrequency (%)
1 770
13.4%
2 1546
27.0%
3 1037
18.1%
4 936
16.3%
5 699
12.2%
6 379
 
6.6%
7 368
 
6.4%
ValueCountFrequency (%)
7 368
 
6.4%
6 379
 
6.6%
5 699
12.2%
4 936
16.3%
3 1037
18.1%
2 1546
27.0%
1 770
13.4%

WTINT2YR
Real number (ℝ)

Distinct4355
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40312.412
Minimum5330.96
Maximum233755.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.9 KiB
2023-06-05T21:50:08.233939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5330.96
5-th percentile8937.22
Q117164.085
median24654.86
Q342862.305
95-th percentile133378.55
Maximum233755.84
Range228424.88
Interquartile range (IQR)25698.22

Descriptive statistics

Standard deviation38768.922
Coefficient of variation (CV)0.96171178
Kurtosis2.902745
Mean40312.412
Median Absolute Deviation (MAD)9946.86
Skewness1.8862699
Sum2.3119168 × 108
Variance1.5030293 × 109
MonotonicityNot monotonic
2023-06-05T21:50:08.666793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24494.47 17
 
0.3%
22264.07 15
 
0.3%
22381.03 14
 
0.2%
14026.93 12
 
0.2%
10454.27 11
 
0.2%
11308.98 11
 
0.2%
24386.55 10
 
0.2%
8842.35 10
 
0.2%
15893.28 10
 
0.2%
10495.87 10
 
0.2%
Other values (4345) 5615
97.9%
ValueCountFrequency (%)
5330.96 1
< 0.1%
5385.79 1
< 0.1%
5743.95 1
< 0.1%
5747.62 2
< 0.1%
5770.49 2
< 0.1%
5861.01 1
< 0.1%
5925.44 1
< 0.1%
5941.62 1
< 0.1%
5965.26 2
< 0.1%
6008.44 1
< 0.1%
ValueCountFrequency (%)
233755.84 1
< 0.1%
232507.96 1
< 0.1%
227372.09 1
< 0.1%
215432.12 1
< 0.1%
214141.24 1
< 0.1%
213887.19 1
< 0.1%
212380.52 1
< 0.1%
209162.64 1
< 0.1%
206028.95 1
< 0.1%
203248.02 1
< 0.1%

SDMVPSU
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.9 KiB
1
2937 
2
2798 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5735
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 2937
51.2%
2 2798
48.8%

Length

2023-06-05T21:50:09.072276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T21:50:09.441603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 2937
51.2%
2 2798
48.8%

Most occurring characters

ValueCountFrequency (%)
1 2937
51.2%
2 2798
48.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5735
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2937
51.2%
2 2798
48.8%

Most occurring scripts

ValueCountFrequency (%)
Common 5735
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2937
51.2%
2 2798
48.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2937
51.2%
2 2798
48.8%

SDMVSTRA
Real number (ℝ)

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.23662
Minimum119
Maximum133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.9 KiB
2023-06-05T21:50:09.802649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum119
5-th percentile119
Q1123
median126
Q3130
95-th percentile133
Maximum133
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.244406
Coefficient of variation (CV)0.033622622
Kurtosis-1.1982164
Mean126.23662
Median Absolute Deviation (MAD)4
Skewness-0.057462975
Sum723967
Variance18.014983
MonotonicityNot monotonic
2023-06-05T21:50:10.170301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
132 446
 
7.8%
125 434
 
7.6%
131 434
 
7.6%
130 391
 
6.8%
121 390
 
6.8%
129 390
 
6.8%
127 389
 
6.8%
128 388
 
6.8%
126 375
 
6.5%
123 374
 
6.5%
Other values (5) 1724
30.1%
ValueCountFrequency (%)
119 297
5.2%
120 344
6.0%
121 390
6.8%
122 366
6.4%
123 374
6.5%
124 366
6.4%
125 434
7.6%
126 375
6.5%
127 389
6.8%
128 388
6.8%
ValueCountFrequency (%)
133 351
6.1%
132 446
7.8%
131 434
7.6%
130 391
6.8%
129 390
6.8%
128 388
6.8%
127 389
6.8%
126 375
6.5%
125 434
7.6%
124 366
6.4%

INDFMPIR
Real number (ℝ)

MISSING  ZEROS 

Distinct440
Distinct (%)8.6%
Missing601
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean2.4032041
Minimum0
Maximum5
Zeros59
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size44.9 KiB
2023-06-05T21:50:10.546382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.34
Q11.06
median1.98
Q33.74
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2.68

Descriptive statistics

Standard deviation1.6019954
Coefficient of variation (CV)0.66660811
Kurtosis-1.1477147
Mean2.4032041
Median Absolute Deviation (MAD)1.11
Skewness0.44666272
Sum12338.05
Variance2.5663891
MonotonicityNot monotonic
2023-06-05T21:50:10.919467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 825
 
14.4%
0 59
 
1.0%
1.49 49
 
0.9%
1.23 45
 
0.8%
1.26 44
 
0.8%
1.24 41
 
0.7%
1.76 41
 
0.7%
0.75 39
 
0.7%
1.65 38
 
0.7%
0.94 37
 
0.6%
Other values (430) 3916
68.3%
(Missing) 601
 
10.5%
ValueCountFrequency (%)
0 59
1.0%
0.01 4
 
0.1%
0.02 18
 
0.3%
0.03 6
 
0.1%
0.04 11
 
0.2%
0.05 4
 
0.1%
0.06 4
 
0.1%
0.07 5
 
0.1%
0.08 14
 
0.2%
0.09 1
 
< 0.1%
ValueCountFrequency (%)
5 825
14.4%
4.99 12
 
0.2%
4.98 9
 
0.2%
4.96 14
 
0.2%
4.95 5
 
0.1%
4.94 11
 
0.2%
4.93 5
 
0.1%
4.92 1
 
< 0.1%
4.91 1
 
< 0.1%
4.89 1
 
< 0.1%

BPXSY1
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct68
Distinct (%)1.3%
Missing334
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean125.08461
Minimum82
Maximum236
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.9 KiB
2023-06-05T21:50:11.247015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum82
5-th percentile100
Q1112
median122
Q3134
95-th percentile160
Maximum236
Range154
Interquartile range (IQR)22

Descriptive statistics

Standard deviation18.480873
Coefficient of variation (CV)0.14774697
Kurtosis1.9180315
Mean125.08461
Median Absolute Deviation (MAD)10
Skewness1.0402496
Sum675582
Variance341.54265
MonotonicityNot monotonic
2023-06-05T21:50:11.521329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116 320
 
5.6%
114 298
 
5.2%
126 281
 
4.9%
124 276
 
4.8%
118 254
 
4.4%
122 240
 
4.2%
120 238
 
4.1%
112 227
 
4.0%
128 225
 
3.9%
106 219
 
3.8%
Other values (58) 2823
49.2%
(Missing) 334
 
5.8%
ValueCountFrequency (%)
82 1
 
< 0.1%
84 6
 
0.1%
86 4
 
0.1%
88 5
 
0.1%
90 11
 
0.2%
92 17
 
0.3%
94 39
0.7%
96 58
1.0%
98 74
1.3%
100 91
1.6%
ValueCountFrequency (%)
236 1
 
< 0.1%
230 1
 
< 0.1%
218 2
 
< 0.1%
210 1
 
< 0.1%
208 1
 
< 0.1%
206 3
0.1%
204 5
0.1%
202 1
 
< 0.1%
200 1
 
< 0.1%
198 3
0.1%

BPXDI1
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)0.9%
Missing334
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean69.516386
Minimum0
Maximum120
Zeros26
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size44.9 KiB
2023-06-05T21:50:11.846888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q162
median70
Q378
95-th percentile90
Maximum120
Range120
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.881575
Coefficient of variation (CV)0.18530271
Kurtosis3.7102355
Mean69.516386
Median Absolute Deviation (MAD)8
Skewness-0.62010569
Sum375458
Variance165.93496
MonotonicityNot monotonic
2023-06-05T21:50:12.210663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 389
 
6.8%
68 379
 
6.6%
72 370
 
6.5%
74 360
 
6.3%
64 345
 
6.0%
76 336
 
5.9%
70 328
 
5.7%
62 284
 
5.0%
78 283
 
4.9%
80 249
 
4.3%
Other values (40) 2078
36.2%
(Missing) 334
 
5.8%
ValueCountFrequency (%)
0 26
0.5%
14 1
 
< 0.1%
22 3
 
0.1%
26 1
 
< 0.1%
28 2
 
< 0.1%
30 3
 
0.1%
32 1
 
< 0.1%
34 3
 
0.1%
36 4
 
0.1%
38 10
 
0.2%
ValueCountFrequency (%)
120 2
 
< 0.1%
116 3
 
0.1%
114 1
 
< 0.1%
112 2
 
< 0.1%
110 3
 
0.1%
108 6
 
0.1%
106 4
 
0.1%
104 8
0.1%
102 15
0.3%
100 17
0.3%

BPXSY2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct67
Distinct (%)1.2%
Missing200
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean124.78302
Minimum84
Maximum238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.9 KiB
2023-06-05T21:50:12.598152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum84
5-th percentile100
Q1112
median122
Q3134
95-th percentile160
Maximum238
Range154
Interquartile range (IQR)22

Descriptive statistics

Standard deviation18.527012
Coefficient of variation (CV)0.14847382
Kurtosis1.9467381
Mean124.78302
Median Absolute Deviation (MAD)12
Skewness1.0442679
Sum690674
Variance343.25016
MonotonicityNot monotonic
2023-06-05T21:50:12.922021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114 315
 
5.5%
116 312
 
5.4%
124 306
 
5.3%
126 275
 
4.8%
118 264
 
4.6%
120 252
 
4.4%
110 230
 
4.0%
128 225
 
3.9%
122 222
 
3.9%
112 219
 
3.8%
Other values (57) 2915
50.8%
ValueCountFrequency (%)
84 2
 
< 0.1%
86 4
 
0.1%
88 11
 
0.2%
90 16
 
0.3%
92 22
 
0.4%
94 40
 
0.7%
96 67
1.2%
98 68
1.2%
100 99
1.7%
102 129
2.2%
ValueCountFrequency (%)
238 1
 
< 0.1%
226 1
 
< 0.1%
220 1
 
< 0.1%
212 2
 
< 0.1%
210 1
 
< 0.1%
208 3
0.1%
206 4
0.1%
202 3
0.1%
200 1
 
< 0.1%
198 5
0.1%

BPXDI2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct49
Distinct (%)0.9%
Missing200
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean69.346703
Minimum0
Maximum144
Zeros33
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size44.9 KiB
2023-06-05T21:50:13.265851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q162
median70
Q378
95-th percentile90
Maximum144
Range144
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.022829
Coefficient of variation (CV)0.18779306
Kurtosis4.4252108
Mean69.346703
Median Absolute Deviation (MAD)8
Skewness-0.66304495
Sum383834
Variance169.59409
MonotonicityNot monotonic
2023-06-05T21:50:13.617517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
68 402
 
7.0%
66 398
 
6.9%
74 381
 
6.6%
72 368
 
6.4%
76 360
 
6.3%
64 356
 
6.2%
78 337
 
5.9%
70 312
 
5.4%
62 272
 
4.7%
60 252
 
4.4%
Other values (39) 2097
36.6%
ValueCountFrequency (%)
0 33
0.6%
24 1
 
< 0.1%
30 3
 
0.1%
32 3
 
0.1%
34 5
 
0.1%
36 3
 
0.1%
38 8
 
0.1%
40 14
0.2%
42 19
0.3%
44 34
0.6%
ValueCountFrequency (%)
144 1
 
< 0.1%
130 1
 
< 0.1%
128 1
 
< 0.1%
118 2
 
< 0.1%
114 5
 
0.1%
112 2
 
< 0.1%
110 1
 
< 0.1%
108 3
 
0.1%
106 4
 
0.1%
104 14
0.2%

BMXWT
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct997
Distinct (%)17.6%
Missing69
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean81.342676
Minimum32.4
Maximum198.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.9 KiB
2023-06-05T21:50:14.256706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum32.4
5-th percentile52.1
Q165.9
median78.2
Q392.7
95-th percentile122.575
Maximum198.9
Range166.5
Interquartile range (IQR)26.8

Descriptive statistics

Standard deviation21.764409
Coefficient of variation (CV)0.26756446
Kurtosis1.6195572
Mean81.342676
Median Absolute Deviation (MAD)13.3
Skewness1.014183
Sum460887.6
Variance473.68951
MonotonicityNot monotonic
2023-06-05T21:50:14.589365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81.4 20
 
0.3%
70.6 20
 
0.3%
81.3 19
 
0.3%
78.2 19
 
0.3%
69.8 19
 
0.3%
69.1 19
 
0.3%
73.2 19
 
0.3%
81.8 18
 
0.3%
67.7 18
 
0.3%
74.7 18
 
0.3%
Other values (987) 5477
95.5%
(Missing) 69
 
1.2%
ValueCountFrequency (%)
32.4 1
< 0.1%
34.8 1
< 0.1%
36 1
< 0.1%
37 1
< 0.1%
39 1
< 0.1%
39.2 1
< 0.1%
39.6 1
< 0.1%
39.7 1
< 0.1%
39.8 1
< 0.1%
39.9 1
< 0.1%
ValueCountFrequency (%)
198.9 1
< 0.1%
192.3 2
< 0.1%
181.5 1
< 0.1%
181 1
< 0.1%
178.9 1
< 0.1%
178.4 1
< 0.1%
178.3 1
< 0.1%
175.9 1
< 0.1%
175.7 1
< 0.1%
175.3 1
< 0.1%

BMXHT
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct509
Distinct (%)9.0%
Missing62
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean166.14283
Minimum129.7
Maximum202.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.9 KiB
2023-06-05T21:50:14.936770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum129.7
5-th percentile150.06
Q1158.7
median166
Q3173.5
95-th percentile182.7
Maximum202.7
Range73
Interquartile range (IQR)14.8

Descriptive statistics

Standard deviation10.079264
Coefficient of variation (CV)0.060666256
Kurtosis-0.433296
Mean166.14283
Median Absolute Deviation (MAD)7.4
Skewness0.082296925
Sum942528.3
Variance101.59156
MonotonicityNot monotonic
2023-06-05T21:50:15.274225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
163.4 30
 
0.5%
168.4 29
 
0.5%
169.6 29
 
0.5%
167.9 29
 
0.5%
159.5 28
 
0.5%
161 28
 
0.5%
166.7 28
 
0.5%
154.7 27
 
0.5%
163.2 27
 
0.5%
166.8 27
 
0.5%
Other values (499) 5391
94.0%
(Missing) 62
 
1.1%
ValueCountFrequency (%)
129.7 1
< 0.1%
136.5 1
< 0.1%
137.4 1
< 0.1%
137.6 1
< 0.1%
137.9 1
< 0.1%
138.4 1
< 0.1%
139 2
< 0.1%
139.6 1
< 0.1%
139.9 1
< 0.1%
140 1
< 0.1%
ValueCountFrequency (%)
202.7 1
< 0.1%
201 1
< 0.1%
198.4 1
< 0.1%
195.6 1
< 0.1%
195.4 1
< 0.1%
195.1 1
< 0.1%
194.6 1
< 0.1%
194.3 1
< 0.1%
193.8 1
< 0.1%
193.7 1
< 0.1%

BMXBMI
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct393
Distinct (%)6.9%
Missing73
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean29.382197
Minimum14.5
Maximum67.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.9 KiB
2023-06-05T21:50:15.581957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14.5
5-th percentile20.2
Q124.3
median28.3
Q333
95-th percentile42.9
Maximum67.3
Range52.8
Interquartile range (IQR)8.7

Descriptive statistics

Standard deviation7.095921
Coefficient of variation (CV)0.2415041
Kurtosis1.9599539
Mean29.382197
Median Absolute Deviation (MAD)4.3
Skewness1.1093945
Sum166362
Variance50.352094
MonotonicityNot monotonic
2023-06-05T21:50:15.916732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.1 52
 
0.9%
26.5 49
 
0.9%
27.8 47
 
0.8%
26.8 45
 
0.8%
26.9 45
 
0.8%
29.4 44
 
0.8%
23.5 44
 
0.8%
31.1 44
 
0.8%
26.6 42
 
0.7%
25.1 42
 
0.7%
Other values (383) 5208
90.8%
(Missing) 73
 
1.3%
ValueCountFrequency (%)
14.5 2
< 0.1%
15.1 1
 
< 0.1%
15.5 1
 
< 0.1%
16 1
 
< 0.1%
16.2 4
0.1%
16.3 1
 
< 0.1%
16.4 2
< 0.1%
16.5 2
< 0.1%
16.6 4
0.1%
16.7 4
0.1%
ValueCountFrequency (%)
67.3 1
< 0.1%
64.6 1
< 0.1%
64.5 2
< 0.1%
63.9 1
< 0.1%
63.6 1
< 0.1%
62.7 1
< 0.1%
61.7 1
< 0.1%
61.1 1
< 0.1%
60.9 1
< 0.1%
60.7 1
< 0.1%

BMXLEG
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct218
Distinct (%)4.1%
Missing390
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean38.576782
Minimum26
Maximum51.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.9 KiB
2023-06-05T21:50:16.249980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile32
Q136
median38.6
Q341.2
95-th percentile44.9
Maximum51.5
Range25.5
Interquartile range (IQR)5.2

Descriptive statistics

Standard deviation3.8730179
Coefficient of variation (CV)0.10039764
Kurtosis-0.1867996
Mean38.576782
Median Absolute Deviation (MAD)2.6
Skewness-0.026852685
Sum206192.9
Variance15.000267
MonotonicityNot monotonic
2023-06-05T21:50:16.630796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 109
 
1.9%
38 104
 
1.8%
40 101
 
1.8%
37 101
 
1.8%
36 98
 
1.7%
41 96
 
1.7%
42 83
 
1.4%
35 78
 
1.4%
36.5 78
 
1.4%
38.5 75
 
1.3%
Other values (208) 4422
77.1%
(Missing) 390
 
6.8%
ValueCountFrequency (%)
26 2
 
< 0.1%
26.4 1
 
< 0.1%
27.2 1
 
< 0.1%
27.4 1
 
< 0.1%
27.6 1
 
< 0.1%
27.7 1
 
< 0.1%
28 5
0.1%
28.4 1
 
< 0.1%
28.5 5
0.1%
28.6 2
 
< 0.1%
ValueCountFrequency (%)
51.5 1
 
< 0.1%
51.1 1
 
< 0.1%
50.5 1
 
< 0.1%
50.1 1
 
< 0.1%
50 1
 
< 0.1%
49.8 2
< 0.1%
49.6 1
 
< 0.1%
49.2 2
< 0.1%
49 2
< 0.1%
48.9 4
0.1%

BMXARML
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct163
Distinct (%)3.0%
Missing308
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean37.146987
Minimum28.2
Maximum47.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.9 KiB
2023-06-05T21:50:17.021109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum28.2
5-th percentile32.6
Q135.2
median37.1
Q339
95-th percentile41.8
Maximum47.4
Range19.2
Interquartile range (IQR)3.8

Descriptive statistics

Standard deviation2.8007838
Coefficient of variation (CV)0.075397333
Kurtosis-0.24494968
Mean37.146987
Median Absolute Deviation (MAD)1.9
Skewness0.066277395
Sum201596.7
Variance7.8443897
MonotonicityNot monotonic
2023-06-05T21:50:17.387230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 199
 
3.5%
36 190
 
3.3%
38 184
 
3.2%
39 139
 
2.4%
35 134
 
2.3%
38.5 125
 
2.2%
34 125
 
2.2%
40 121
 
2.1%
37.5 110
 
1.9%
36.5 106
 
1.8%
Other values (153) 3994
69.6%
(Missing) 308
 
5.4%
ValueCountFrequency (%)
28.2 1
 
< 0.1%
29.1 1
 
< 0.1%
29.4 1
 
< 0.1%
29.5 3
0.1%
29.6 1
 
< 0.1%
29.7 2
< 0.1%
30 4
0.1%
30.1 2
< 0.1%
30.2 4
0.1%
30.3 2
< 0.1%
ValueCountFrequency (%)
47.4 1
< 0.1%
47 1
< 0.1%
46.5 1
< 0.1%
46.3 1
< 0.1%
46.2 1
< 0.1%
46 2
< 0.1%
45.9 1
< 0.1%
45.4 1
< 0.1%
45.3 1
< 0.1%
45.2 2
< 0.1%

BMXARMC
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct297
Distinct (%)5.5%
Missing308
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean33.112235
Minimum17.1
Maximum58.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.9 KiB
2023-06-05T21:50:17.789664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum17.1
5-th percentile25.1
Q129.5
median32.7
Q336.2
95-th percentile42.57
Maximum58.4
Range41.3
Interquartile range (IQR)6.7

Descriptive statistics

Standard deviation5.2680275
Coefficient of variation (CV)0.1590961
Kurtosis0.44939796
Mean33.112235
Median Absolute Deviation (MAD)3.4
Skewness0.5179575
Sum179700.1
Variance27.752113
MonotonicityNot monotonic
2023-06-05T21:50:18.178077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34 74
 
1.3%
33 67
 
1.2%
33.5 66
 
1.2%
32.5 62
 
1.1%
31.5 60
 
1.0%
31.2 59
 
1.0%
31 57
 
1.0%
36 57
 
1.0%
30 57
 
1.0%
32.2 55
 
1.0%
Other values (287) 4813
83.9%
(Missing) 308
 
5.4%
ValueCountFrequency (%)
17.1 1
 
< 0.1%
18.9 1
 
< 0.1%
19.7 1
 
< 0.1%
19.9 1
 
< 0.1%
20.2 5
0.1%
20.3 2
 
< 0.1%
21 3
0.1%
21.1 1
 
< 0.1%
21.4 2
 
< 0.1%
21.5 1
 
< 0.1%
ValueCountFrequency (%)
58.4 1
< 0.1%
55.8 1
< 0.1%
54.4 1
< 0.1%
53 1
< 0.1%
52.6 1
< 0.1%
52.5 2
< 0.1%
52.2 1
< 0.1%
52 1
< 0.1%
51.7 1
< 0.1%
51 1
< 0.1%

BMXWAIST
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct794
Distinct (%)14.8%
Missing367
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean99.567213
Minimum58.7
Maximum171.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.9 KiB
2023-06-05T21:50:18.539259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum58.7
5-th percentile74.2
Q187.6
median98.3
Q3109.3
95-th percentile130.9
Maximum171.6
Range112.9
Interquartile range (IQR)21.7

Descriptive statistics

Standard deviation16.844109
Coefficient of variation (CV)0.16917325
Kurtosis0.45548878
Mean99.567213
Median Absolute Deviation (MAD)10.9
Skewness0.58684489
Sum534476.8
Variance283.72399
MonotonicityNot monotonic
2023-06-05T21:50:18.926679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 27
 
0.5%
97 27
 
0.5%
93.5 25
 
0.4%
94 25
 
0.4%
84 22
 
0.4%
99 22
 
0.4%
102.7 21
 
0.4%
103.2 20
 
0.3%
92.5 20
 
0.3%
99.2 20
 
0.3%
Other values (784) 5139
89.6%
(Missing) 367
 
6.4%
ValueCountFrequency (%)
58.7 1
< 0.1%
62.1 1
< 0.1%
63 1
< 0.1%
63.4 1
< 0.1%
64 2
< 0.1%
64.2 1
< 0.1%
64.5 2
< 0.1%
64.9 1
< 0.1%
65 1
< 0.1%
65.2 1
< 0.1%
ValueCountFrequency (%)
171.6 1
< 0.1%
169.6 2
< 0.1%
165 1
< 0.1%
164 1
< 0.1%
162.7 1
< 0.1%
161.5 1
< 0.1%
161.3 1
< 0.1%
160.5 1
< 0.1%
160.2 1
< 0.1%
160 1
< 0.1%

HIQ210
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)0.1%
Missing1003
Missing (%)17.5%
Memory size44.9 KiB
2.0
4268 
1.0
456 
9.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters14196
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 4268
74.4%
1.0 456
 
8.0%
9.0 8
 
0.1%
(Missing) 1003
 
17.5%

Length

2023-06-05T21:50:19.307238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T21:50:19.616676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 4268
90.2%
1.0 456
 
9.6%
9.0 8
 
0.2%

Most occurring characters

ValueCountFrequency (%)
. 4732
33.3%
0 4732
33.3%
2 4268
30.1%
1 456
 
3.2%
9 8
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9464
66.7%
Other Punctuation 4732
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4732
50.0%
2 4268
45.1%
1 456
 
4.8%
9 8
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 4732
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14196
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4732
33.3%
0 4732
33.3%
2 4268
30.1%
1 456
 
3.2%
9 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14196
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4732
33.3%
0 4732
33.3%
2 4268
30.1%
1 456
 
3.2%
9 8
 
0.1%

Interactions

2023-06-05T21:49:44.138262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:46:57.794381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:07.712147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:17.392419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:26.941381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:35.217607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:44.401024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:52.691839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:01.873268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:11.150200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:20.248384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:29.817827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:38.984568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:48.881130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:58.482885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:07.253346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:12.886615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:21.476231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:28.084040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:34.403402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:44.615986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:46:58.247954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:08.387048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:17.889205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:27.316232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:35.633328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:44.775104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:53.275737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:02.324006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:11.552883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:20.697078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:30.265571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:39.362916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:49.341233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:58.923256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:07.554358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:13.207384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:21.949643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:28.372081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:34.824358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:45.327861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:46:58.721196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:08.841679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:18.153544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:27.774505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:36.078821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:45.099641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:53.668853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:02.785415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:12.031453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:21.078361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:30.628400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:39.897144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:49.867400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:59.422813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:07.815881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:13.596213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:22.389315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:28.672240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:35.299136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:45.754659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:46:59.199095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:09.384003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:18.608860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:28.113928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:36.526958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:45.372429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:54.104679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:03.258730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:12.520635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:21.865465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:31.151470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:40.348420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:50.388198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:59.915608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:08.069664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:13.996122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:22.872604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:28.954295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:36.005290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:46.212271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:46:59.788996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:09.779270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:19.657048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:28.544049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:37.040817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:45.769176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:54.551407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:03.741431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:12.953975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:22.293481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:31.662627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:40.771882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:50.856254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:00.416425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:08.366352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:14.422026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:23.247739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:29.271227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:36.479071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:46.703074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:00.441241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:10.259470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:20.059756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:29.060513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:37.856441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:46.202767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:54.960375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:04.199187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:13.356478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:22.843388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:32.203360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:41.277155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:51.342923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:00.889530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:08.652675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:14.717595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:23.553149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:29.589698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:36.952456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:47.151957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:00.940864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:10.816962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:20.642756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:29.461316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:38.237733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:46.558641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:55.498754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:04.628183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:13.751789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:23.284515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:32.636869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:41.756131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:51.843814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:01.364063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:08.931810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:15.129042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:23.865651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:29.856377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:37.423362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:47.624795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:01.442426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:11.359958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:21.210405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:29.887836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:38.820194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:46.889100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:55.823974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:05.021643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:14.169710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:23.631090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:33.021832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:42.206238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:52.319499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:01.854593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:09.223777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:15.493784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:24.127223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:30.129340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:37.854316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:48.073697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:01.899394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:11.891670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:21.670963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:30.343451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:39.261317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:47.295524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:56.188789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:05.574858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:14.553402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:24.144077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:33.516302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:42.685733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:52.770349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:02.374015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:09.533384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:15.941420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:24.434015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:30.458159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:38.288249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:48.575253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:02.377532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:12.349182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:21.950943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:30.758642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:39.730391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:47.691053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:56.557524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:06.012685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:14.989706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:24.556577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:33.905442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:43.156171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:53.233986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:02.885536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:09.805121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:16.358490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:24.718519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:30.722972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:38.803930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:49.083490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:02.909759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:12.816010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:22.424165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:31.158635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:40.145961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:48.167124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:56.950324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:06.527427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:15.417976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:25.075785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:34.405553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:44.006144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:53.679903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:03.420891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:10.125612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:16.864507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:25.264043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:31.027790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:39.297410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:49.588849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:03.376658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:13.350033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:22.843015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:31.527165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:40.666261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:48.605442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:57.352105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:06.941640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:15.909313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:25.563704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:34.751294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:44.400337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:54.305925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:03.875399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:10.415335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:17.287464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:25.525190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:31.320551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:39.750498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:50.118028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:03.856449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:13.965110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:23.454220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:32.003000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:41.095889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:49.125091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:57.768162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:07.469577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:16.344556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:26.020291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:35.246889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:44.951160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:54.776860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:04.282604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:10.691116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:17.782198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:25.816832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:31.619874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:40.238539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:50.598904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:04.345556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:14.439355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:23.935329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:32.474422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:41.501798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:49.630827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:58.660727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:07.941780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:16.937785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:26.457928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:35.698579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:45.445880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:55.256128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:04.708462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:10.949368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:18.253829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:26.101561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:31.934891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:40.700331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:51.076221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:04.838055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:14.905386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:24.381788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:32.870817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:41.988956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:50.096328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:59.022076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:08.351397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:17.445362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:27.004850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:36.271794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:45.938192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:55.696816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:05.080102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:11.238836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:18.698823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:26.421040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:32.261158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:41.198214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:51.616261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:05.336356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:15.298735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:24.724804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:33.292249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:42.472568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:50.507031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:59.494175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:08.886398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:17.927698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:27.538913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:36.736173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:46.430378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:56.195026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:05.523277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:11.507331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:19.179357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:26.710359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:32.557948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:41.608138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:52.116384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:05.776711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:15.639444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:25.161733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:33.738168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:42.827909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:50.875613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:59.894910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:09.277854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:18.359848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:27.992978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:37.126330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:46.891063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:56.605092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:05.849607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:11.792696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:19.592818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:26.972332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:32.815678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:42.012293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:52.602956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:06.222974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:15.910761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:25.608114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:34.126795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:43.200408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:51.341888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:00.326421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:09.735430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:18.950746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:28.444566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:37.622997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:47.418985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:57.073591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:06.171131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:12.029896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:20.099631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:27.255649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:33.138259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:42.558818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:53.125752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:06.789994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:16.225496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:26.078434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:34.553888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:43.655604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:51.825033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:00.848795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:10.277864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:19.444046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:28.915344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:38.138205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:47.892879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:57.573273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:06.703531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:12.347395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:20.557293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:27.561291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:33.475359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:43.214657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:53.566980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:07.238887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:16.936813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:26.536881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:34.860375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:43.966454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:47:52.289997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:01.324554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:10.715517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:19.881896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:29.385101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:38.562238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:48.328268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:48:58.046384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:06.997165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:12.617859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:21.071700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:27.789849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:33.817346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T21:49:43.686198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-05T21:50:19.855497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
SEQNALQ130RIDAGEYRDMDEDUC2DMDMARTLDMDHHSIZWTINT2YRSDMVSTRAINDFMPIRBPXSY1BPXDI1BPXSY2BPXDI2BMXWTBMXHTBMXBMIBMXLEGBMXARMLBMXARMCBMXWAISTALQ101ALQ110SMQ020RIAGENDRRIDRETH1DMDCITZNSDMVPSUHIQ210
SEQN1.000-0.0230.0020.0200.001-0.024-0.001-0.0060.0090.0100.0170.0060.0190.0110.0090.0100.0170.0200.015-0.0010.0190.0090.0270.0000.0000.0000.0000.000
ALQ130-0.0231.000-0.254-0.2220.1560.1160.004-0.001-0.146-0.0040.0460.0040.0330.1190.1910.0390.2060.1350.1250.0370.0631.0000.0000.0000.0570.0000.0000.000
RIDAGEYR0.002-0.2541.000-0.130-0.292-0.392-0.2200.0260.0330.4820.0120.459-0.0110.019-0.1380.102-0.3050.027-0.0260.2600.0820.0830.1200.0310.1050.1070.0000.112
DMDEDUC20.020-0.222-0.1301.000-0.056-0.1490.2500.0030.464-0.1470.025-0.1330.0390.0220.171-0.0800.1610.079-0.025-0.0830.1060.4160.2580.0640.2130.1900.1380.035
DMDMARTL0.0010.156-0.292-0.0561.000-0.1170.041-0.008-0.212-0.066-0.031-0.062-0.031-0.0050.026-0.0220.1200.016-0.003-0.0760.0001.0000.0000.0000.0110.0000.0000.000
DMDHHSIZ-0.0240.116-0.392-0.149-0.1171.000-0.080-0.049-0.152-0.1850.018-0.1770.026-0.015-0.0540.0140.015-0.0910.041-0.0660.0760.0710.0790.0170.1410.1260.0400.065
WTINT2YR-0.0010.004-0.2200.2500.041-0.0801.0000.0450.302-0.1680.041-0.1500.0570.1080.2060.0030.1730.1170.0600.0150.1370.0000.0460.1230.3880.1270.0880.030
SDMVSTRA-0.006-0.0010.0260.003-0.008-0.0490.0451.0000.0060.0280.0170.0400.0270.0490.0270.0400.0270.0580.0500.0510.0460.0420.0710.0070.2700.1050.1090.014
INDFMPIR0.009-0.1460.0330.464-0.212-0.1520.3020.0061.000-0.0360.069-0.0370.0760.0350.163-0.0510.1200.091-0.002-0.0300.0950.1050.0580.0410.1380.1130.0790.104
BPXSY10.010-0.0040.482-0.147-0.066-0.185-0.1680.028-0.0361.0000.3500.9500.3140.1670.0160.186-0.0660.1310.1470.2680.0840.0800.0730.1430.0470.0320.0410.026
BPXDI10.0170.0460.0120.025-0.0310.0180.0410.0170.0690.3501.0000.3570.8980.1500.1210.1030.0790.1070.1390.1160.0730.0000.0000.1040.0530.0000.0450.000
BPXSY20.0060.0040.459-0.133-0.062-0.177-0.1500.040-0.0370.9500.3571.0000.3400.1840.0220.201-0.0600.1400.1690.2760.0860.0660.0830.1390.0540.0290.0140.018
BPXDI20.0190.033-0.0110.039-0.0310.0260.0570.0270.0760.3140.8980.3401.0000.1440.1170.0990.0730.0990.1340.1050.0520.0200.0720.0940.0640.0000.0490.014
BMXWT0.0110.1190.0190.022-0.005-0.0150.1080.0490.0350.1670.1500.1840.1441.0000.4480.8650.3300.5630.9070.8720.0930.0440.0730.2740.1200.0850.0580.042
BMXHT0.0090.191-0.1380.1710.026-0.0540.2060.0270.1630.0160.1210.0220.1170.4481.000-0.0220.7830.7890.2610.1410.2120.0540.1080.6810.1520.0990.0000.037
BMXBMI0.0100.0390.102-0.080-0.0220.0140.0030.040-0.0510.1860.1030.2010.0990.865-0.0221.000-0.0480.2090.8740.9080.0000.0170.0000.1310.1210.0500.0840.052
BMXLEG0.0170.206-0.3050.1610.1200.0150.1730.0270.120-0.0660.079-0.0600.0730.3300.783-0.0481.0000.6310.2080.0140.1680.0000.0540.5300.1640.0740.0560.000
BMXARML0.0200.1350.0270.0790.016-0.0910.1170.0580.0910.1310.1070.1400.0990.5630.7890.2090.6311.0000.4380.3460.1490.1640.1020.5540.1490.0960.0190.035
BMXARMC0.0150.125-0.026-0.025-0.0030.0410.0600.050-0.0020.1470.1390.1690.1340.9070.2610.8740.2080.4381.0000.7980.0520.0390.0410.2120.1070.0580.0640.035
BMXWAIST-0.0010.0370.260-0.083-0.076-0.0660.0150.051-0.0300.2680.1160.2760.1050.8720.1410.9080.0140.3460.7981.0000.0000.0530.0760.0880.1200.0740.0610.067
ALQ1010.0190.0630.0820.1060.0000.0760.1370.0460.0950.0840.0730.0860.0520.0930.2120.0000.1680.1490.0520.0001.0000.0240.2160.2590.1190.0790.0190.022
ALQ1100.0091.0000.0830.4161.0000.0710.0000.0420.1050.0800.0000.0660.0200.0440.0540.0170.0000.1640.0390.0530.0241.0000.7420.0000.0640.0900.0900.000
SMQ0200.0270.0000.1200.2580.0000.0790.0460.0710.0580.0730.0000.0830.0720.0730.1080.0000.0540.1020.0410.0760.2160.7421.0000.2120.0930.0610.0200.000
RIAGENDR0.0000.0000.0310.0640.0000.0170.1230.0070.0410.1430.1040.1390.0940.2740.6810.1310.5300.5540.2120.0880.2590.0000.2121.0000.0460.0170.0000.029
RIDRETH10.0000.0570.1050.2130.0110.1410.3880.2700.1380.0470.0530.0540.0640.1200.1520.1210.1640.1490.1070.1200.1190.0640.0930.0461.0000.2420.2620.063
DMDCITZN0.0000.0000.1070.1900.0000.1260.1270.1050.1130.0320.0000.0290.0000.0850.0990.0500.0740.0960.0580.0740.0790.0900.0610.0170.2421.0000.0620.058
SDMVPSU0.0000.0000.0000.1380.0000.0400.0880.1090.0790.0410.0450.0140.0490.0580.0000.0840.0560.0190.0640.0610.0190.0900.0200.0000.2620.0621.0000.047
HIQ2100.0000.0000.1120.0350.0000.0650.0300.0140.1040.0260.0000.0180.0140.0420.0370.0520.0000.0350.0350.0670.0220.0000.0000.0290.0630.0580.0471.000

Missing values

2023-06-05T21:49:54.332771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-05T21:49:55.890668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-05T21:49:57.262864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SEQNALQ101ALQ110ALQ130SMQ020RIAGENDRRIDAGEYRRIDRETH1DMDCITZNDMDEDUC2DMDMARTLDMDHHSIZWTINT2YRSDMVPSUSDMVSTRAINDFMPIRBPXSY1BPXDI1BPXSY2BPXDI2BMXWTBMXHTBMXBMIBMXLEGBMXARMLBMXARMCBMXWAISTHIQ210
0837321.0NaN1.0116231.05.01.02134671.3711254.39128.070.0124.064.094.8184.527.843.343.635.9101.12.0
1837331.0NaN6.0115332.03.03.0124328.5611251.32146.088.0140.088.090.4171.430.838.040.033.2107.9NaN
2837341.0NaNNaN117831.03.01.0212400.0111311.51138.046.0132.044.083.4170.128.835.637.031.0116.52.0
3837352.01.01.0225631.05.06.01102718.0011315.00132.072.0134.068.0109.8160.942.438.537.738.3110.12.0
4837362.01.01.0224241.04.03.0517627.6721261.23100.070.0114.054.055.2164.920.337.436.027.280.42.0
5837372.02.0NaN227212.02.04.0511252.3111282.82116.058.0122.058.064.4150.028.634.433.531.492.9NaN
6837411.0NaN8.0112241.04.05.0337043.0921282.08110.070.0112.074.076.6165.428.038.838.034.086.6NaN
7837421.0NaN1.0223212.04.01.0422744.3611251.03120.070.0114.070.064.5151.328.234.133.131.593.32.0
883743NaNNaNNaN211851.0NaNNaN318526.1621225.00NaNNaNNaNNaN72.4166.126.2NaNNaNNaNNaN2.0
9837441.0NaNNaN215641.03.03.0120395.5421261.19178.0116.0180.0114.0108.3179.433.646.044.138.5116.02.0
SEQNALQ101ALQ110ALQ130SMQ020RIAGENDRRIDAGEYRRIDRETH1DMDCITZNDMDEDUC2DMDMARTLDMDHHSIZWTINT2YRSDMVPSUSDMVSTRAINDFMPIRBPXSY1BPXDI1BPXSY2BPXDI2BMXWTBMXHTBMXBMIBMXLEGBMXARMLBMXARMCBMXWAISTHIQ210
5725936842.02.0NaN213441.05.01.0529880.6421312.81110.072.0112.072.0101.2180.930.943.743.041.399.02.0
5726936851.0NaN2.0115312.02.01.0522441.8611260.49132.054.0128.056.078.7156.932.031.538.033.7107.52.0
5727936892.01.0NaN226911.01.01.019611.1921270.97164.062.0166.064.064.8151.928.132.232.628.7101.12.0
5728936901.0NaN3.0213221.02.01.0443971.6021275.00112.060.0118.058.089.5164.932.940.038.039.0101.02.0
5729936912.02.0NaN212552.05.05.0713525.3921331.59112.080.0112.076.039.2136.521.033.629.723.875.42.0
5730936952.02.0NaN127631.03.02.0158614.0821301.43112.048.0112.046.059.1165.821.538.237.029.595.02.0
5731936962.02.0NaN212631.05.01.03122920.6011212.99118.068.0116.076.0112.1182.233.843.441.842.3110.22.0
5732936971.0NaN1.0128031.04.02.0149050.0621322.97154.056.0146.058.071.7152.231.031.337.528.8NaN2.0
573393700NaNNaNNaN113532.01.01.0542314.2911260.00104.062.0106.066.078.2173.326.040.337.530.698.92.0
5734937021.0NaN2.0222431.05.05.03107361.9121193.54118.066.0114.068.058.3165.021.438.233.526.272.52.0